001484474 000__ 06295cam\\2200625Mu\4500
001484474 001__ 1484474
001484474 003__ OCoLC
001484474 005__ 20240117003325.0
001484474 006__ m\\\\\o\\d\\\\\\\\
001484474 007__ cr\cn\nnnunnun
001484474 008__ 231209s2023\\\\sz\a\\\\o\\\\\100\0\eng\d
001484474 019__ $$a1411278025
001484474 020__ $$a9783031424137
001484474 020__ $$a3031424131
001484474 020__ $$z3031424123
001484474 020__ $$z9783031424120
001484474 0247_ $$a10.1007/978-3-031-42413-7$$2doi
001484474 035__ $$aSP(OCoLC)1411311621
001484474 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dOCLCO$$dYDX
001484474 049__ $$aISEA
001484474 050_4 $$aQA279.5
001484474 08204 $$a519.5/42$$223/eng/20231211
001484474 1112_ $$aBayesian Young Statisticians Meeting$$n(6th :$$d2022 :$$cMontréal, Québec).
001484474 24510 $$aBayesian statistics, new generations new approaches :$$bBAYSM 2022, Montréal, Canada, June 22-23 /$$cAlejandra Avalos-Pacheco, Roberta De Vito, Florian Maire, editors.
001484474 24630 $$aBAYSM 2022
001484474 260__ $$aCham :$$bSpringer International Publishing AG,$$c2023.
001484474 300__ $$a1 online resource (viii, 115 pages) :$$billustrations (some color).
001484474 4901_ $$aSpringer Proceedings in Mathematics and Statistics Series ;$$vv.435
001484474 504__ $$aReferences -- Extended Stochastic Block Model with Spatial Covariates for Weighted Brain Networks -- 1 Introduction -- 2 Poisson Extended Stochastic Block Model -- 2.1 Cohesion Function -- 2.2 Posterior Inference -- 3 Application to Brain Networks -- 3.1 Uncertainty Quantification -- 4 Discussion -- References -- Approximate Bayesian Inference for Smoking Habit Dynamics in Tuscany -- 1 Introduction -- 2 Smoking Habit Compartmental Model -- 2.1 The Probabilistic Model -- 2.2 Approximate Bayesian Computation for the SHC Model -- 3 Results -- 4 Discussion -- References
001484474 5058_ $$aIntro -- Preface -- Contents -- Bayesian Emulation of Complex Computer Models with Structured Partial Discontinuities -- 1 Introduction -- 2 Bayesian Emulation with Partial Discontinuities -- 2.1 Emulation of Computer Models -- 2.2 Torn Embeddings in Higher Dimensions -- 2.3 Controlling for the Induced Local Warping Effect -- 2.4 Controlling for the Global Impact of the Embedding Using Non-stationary Emulation -- 3 Application: TNO OLYMPUS Well Placement Optimisation Challenge -- 4 Conclusion -- References -- A Variational Bayes Approach to Factor Analysis -- 1 Background -- 2 Methods
001484474 5058_ $$a3 Results -- 4 Conclusions -- References -- Scalable Model Selection for Staged Trees: Mean-posterior Clustering and Binary Trees -- 1 Introduction -- 2 Preliminaries -- 2.1 Staged Trees -- 2.2 Conjugate Learning and Model Selection -- 3 Methods -- 3.1 Totally Ordered Hyperstage -- 4 Mean Posterior Probabilities -- 4.1 Resize Operator -- 5 A Comparative Analysis of Competing Methodologies -- 6 Christchurch Health and Development Study Example -- 7 Discussion -- References -- Speeding up the Zig-Zag Process -- 1 Introduction -- 2 The SUZZ Process -- 3 Theoretical Results -- 4 Numerical Examples
001484474 5058_ $$aMixing Times of a Gibbs Sampler for Probit Hierarchical Models -- 1 Introduction -- 2 Probit Hierarchical Models -- 3 Theoretical Results on Mixing Times -- 4 Numerical Illustration -- 5 Conclusions -- References -- A Note on the Dependence Structure of Hierarchical Completely Random Measures -- 1 Introduction -- 2 Hierarchical Completely Random Measures -- 3 Dependence Structure -- 4 Discussion -- 5 Proofs -- References -- Observed Patterns of Heat Wave Intensities with Respect to Time and Global Surface Temperature -- 1 Introduction -- 2 Methods -- 3 Applications
001484474 5058_ $$a3.1 Heat Wave Maximum Intensity Over Time -- 3.2 Heat Wave Maximum Intensity and Global Surface Temperature -- 4 Conclusions -- References -- Expectation Propagation for the Smoothing Distribution in Dynamic Probit -- 1 Introduction -- 2 Literature Review -- 3 Expectation Propagation (EP) for the Dynamic Probit -- 3.1 Implementation Without p n times p npntimespn Matrix Inversions -- 3.2 Implementation Without p n times p npntimespn Matrix Updates -- 3.3 Computational Costs -- 4 Financial Illustration -- 5 Discussion -- References
001484474 506__ $$aAccess limited to authorized users.
001484474 520__ $$aThis book hosts the results presented at the 6th Bayesian Young Statisticians Meeting 2022 in Montral, Canada, held on June 2223, titled "Bayesian Statistics, New Generations New Approaches". This collection features selected peer-reviewed contributions that showcase the vibrant and diverse research presented at meeting. This book is intended for a broad audience interested in statistics and aims at providing stimulating contributions to theoretical, methodological, and computational aspects of Bayesian statistics. The contributions highlight various topics in Bayesian statistics, presenting promising methodological approaches to address critical challenges across diverse applications. This compilation stands as a testament to the talent and potential within the j-ISBA community. This book is meant to serve as a catalyst for continued advancements in Bayesian methodology and its applications and encourages fruitful collaborations that push the boundaries of statistical research.
001484474 588__ $$aDescription based upon print version of record.
001484474 650_6 $$aThéorie de la décision bayésienne$$vCongrès.
001484474 650_0 $$aBayesian statistical decision theory$$vCongresses.$$0(DLC)sh 85012506
001484474 655_0 $$aElectronic books.
001484474 655_7 $$aproceedings (reports)$$2aat
001484474 655_7 $$aConference papers and proceedings.$$2lcgft
001484474 655_7 $$aActes de congrès.$$2rvmgf
001484474 7001_ $$aAvalos-Pacheco, Alejandra.
001484474 7001_ $$aDe Vito, Roberta.
001484474 7001_ $$aMaire, Florian.
001484474 77608 $$iPrint version:$$aAvalos-Pacheco, Alejandra$$tBayesian Statistics, New Generations New Approaches$$dCham : Springer International Publishing AG,c2024$$z9783031424120
001484474 830_0 $$aSpringer proceedings in mathematics & statistics ;$$vv.435.
001484474 852__ $$bebk
001484474 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-42413-7$$zOnline Access$$91397441.1
001484474 909CO $$ooai:library.usi.edu:1484474$$pGLOBAL_SET
001484474 980__ $$aBIB
001484474 980__ $$aEBOOK
001484474 982__ $$aEbook
001484474 983__ $$aOnline
001484474 994__ $$a92$$bISE